Arti: Yeah, so I think for us, we think about a triple mandate around responsible AI, and that means that it’s secure, it’s efficient, and that includes environmentally efficient, and then it really preserves human safety and human oversight above all. And for us, that’s what some of our core pillars of responsible AI include. And for us, that means we think about internally as we build tools, a multi-layered governance approach where we’ve got both for how we use AI as a company and then how we deploy AI into our products. We’ve got a similar framework that we leverage across both, a kind of joint governance model, both around how we’re leveraging AI and also how we’re infusing AI into our products to allow our customers to leverage AI in their operations.

But I would say one of the core foundational principles underneath all of these is that human beings still remain centered in how we think about how AI is leveraged. And so human judgment, human responsibility, human ethics are still core to how we think about where AI can add benefits, and we think about it more as augmenting rather than replacing human beings in critical decision loops, if you will.

Megan: Right, which is such an important distinction, isn’t it? And in terms of the next phase of development, there’s growing sentiment that agentic AI will soon allow these industrial systems to operate much more autonomously. I wonder, what makes the risks in industrial settings different from AI risk in a purely digital context? And I suppose on the flip side, what are the benefits and opportunities of that same software defined automation?

Arti: I think we can start with the risks and then go to the benefits. I think ultimately, I would say the biggest risk in the industrial setting is that we are interacting with real physical systems, often physical systems that are very capable of delivering important outcomes that keep our world running, whether that’s delivering power, whether that’s mining for natural resources. But usually. Those physical systems are also operating in hazardous environments, the equipment itself is capable of also having human safety implications. Anytime you’re interfacing software with a physical system that can have real world consequences, you have to be extra careful. And we’ve always had that, again, we’ve had that in our DNA at AVEVA from the beginning, being mindful of that end user and that end application, which is not just something on a computer screen.

Even as we’ve deployed AI over the past few decades into our systems, we’ve typically leaned toward really trying to pick the best fit model, really understand that model’s behavior if we’re going to use, for example, we have a proprietary anomaly detection model that works in a lot of operational environments. Now, as we’re thinking about some of these newer AI capabilities, which by design are hard to understand how they behave, they’re not fundamentally explainable in the way we’ve thought about even AI or statistical models in the past, and actually their behavior can change over time as they learn and get tuned to new capabilities. That’s really the potential risk of automating a physical system based on capabilities that can evolve over time is one of the things that we have to be really mindful of and really thoughtful around, where are we willing to put some of that increased automation into practice?

But at the same time, I think there’s a real opportunity there, because I glossed over this idea that, okay, well, models evolve, but so do human beings, and sometimes that’s a good thing. We talk about humans having expertise and they gain understanding over their careers of how a system works. If we can actually leverage the ability of some of these newer AI capabilities, these sort of reasoning models often have underlying agentic capabilities, to learn and gather experience faster, and potentially even more importantly or more valuably, take experience that’s learned at one site and apply it to another site, then that’s a real opportunity to take what we already know works for human beings, which is that sometimes you just have to learn by doing and be able to apply that and scale that through AI. That’s where I see potentially a real opportunity in this space.

One of the things that we’re really aware of in the industrial sector is just the way that our workforce is changing. I think that almost half, not quite half, of the industrial workforce is set to retire in the next five years, and that’s a lot of expertise and experience that means that we’re going to lose in the sector. If there’s ways to make sure that we can capture that in ways that are actionable, in ways that can also help a newer generation of workers that are used to experience things in a different way, apply that expertise, apply that knowledge, then I think that’s a huge opportunity within the industrial space.

Megan: Yeah, absolutely. Clearly, some huge opportunities there particularly against the backdrop of other market and workforce changes as you’ve outlined there. I suppose building on that, what is the potential for these autonomous industrial AI systems when built and deployed responsibly to facilitate even faster, more sustainable industrial processes?